Computerized Paleographic Investigation of Hebrew Iron Age Ostraca
Bibliographic record
Abstract
This article surveys ongoing research of the Legibility Enhancement of Ostraca (LEO) team of Tel Aviv University in the field of computerized paleography of Hebrew Iron Age ink-written ostraca. We perform paleographic tasks using tools from the fields of image processing and machine learning. Several new techniques serving this aim, as well as an adaptation of existing ones, are described herein. This includes testing a range of signal-acquisition methodologies, out of which multispectral imaging and Raman spectroscopy have matured into imaging systems. In addition, we deal with semior fully automated facsimile construction and refinement, facsimile, and character evaluation, as well as the reconstruction of broken character strokes. We conclude with future research directions, addressing some of the long-standing epigraphic questions, such as the number of scribes in specific corpora or detection of chronological concurrences and inconsistencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".